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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.14845111 |
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Table of Contents:
- <h1>Ensemble-weighted Deep CNN for Visual Sentiment Analysis - v1.0.0</h1> <h2>New Features</h2> <ul> <li>Implemented Deep Convolutional Neural Network (DCNN) for automatic feature extraction</li> <li>Added ensemble learning with majority, average, and weighted voting for improved accuracy</li> <li>Achieved 70% accuracy on the AffectNet dataset</li> </ul> <h2>Enhancements</h2> <ul> <li>Optimized training pipeline with config-based model selection</li> <li>Improved training scripts for easy model training and evaluation</li> <li>Added support for ensemble-based sentiment analysis</li> </ul> <h2>Installation & Usage</h2> <ol> <li>Clone the repository:<pre><code>git clone https://github.com/katikapalli/ensemble-vsa.git cd ensemble-vsa </code></pre> </li> </ol>